Characterization of human trabecular bone across multiple length scales using a correlative approach combining X-ray tomography with LaserFIB and plasma FIB-SEM
Bibliographic record
Abstract
Three-dimensional correlative multimodal and multiscale imaging is an emerging method for investigating the complex hierarchical structure of biological materials such as bone. This approach synthesizes images acquired across multiple length scales, for the same region of interest, to provide a comprehensive view of the material structure of a sample. Here, we develop a workflow for the structural analysis of human trabecular bone using a femtosecond laser to produce a precise grid to facilitate correlation between imaging modalities and identification of structures of interest, in this case, a single trabecula within a volume of trabecular bone. Through such image registration, high resolution X-ray microscopy imaging revealed fine architectural details, including the cement sheath and bone cell lacunae of the selected bone trabecula. The selected bone volume was exposed with a combination of manual polishing and site-specific femtosecond laser ablation and then examined with plasma focused ion beam-scanning electron microscopy. This reliable and versatile correlation approach has the potential to be applied to a variety of biological tissues and traditional engineered materials. The proposed workflow has the enhanced capability for generating highly resolved and broadly contextualized structural data for a better understanding of the architectural features of a material spanning its macroscopic to nanoscopic levels.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".